Predicting security response impact

ABSTRACT

An approach to predicting the outcome of a computer security response. The approach can analyze an unlabeled set of network data and based on the analysis, create a language model of the network. The approach can process the language model to predict a reduction factor associated with network availability. The approach can further process the language model and a malicious sequence to predict an effectiveness factor associated with blocking the malicious sequence. The approach can output bot the reduction factor and the effectiveness factor to a network administrator for determining the applicability of the computer security response.

TECHNICAL FIELD

The present invention relates generally to computer security, and morespecifically, to predicting security response effectiveness and impacton availability.

BACKGROUND

The frequent occurrence of computer system breaches has highlighted therequirement for adequate security in every system, based on the exposureof sensitive personal information. Computer security administrators arerequired to provide a response for every security incident occurring ina computer system. Estimating incident response effectiveness and thepossible damage of the response to computer system availability isnecessary input for a computer security administrator in making aresponse decision. Research has shown there is a lack of automatic toolsfor estimation of a security breach response's effectiveness and theassociated damage to the availability of the computer system imposed bythe implementation of the response.

BRIEF SUMMARY

According to an embodiment of the present invention, acomputer-implemented method for evaluating a computer security response,the computer-implemented method comprising: analyzing an unlabeled setof network data to create a language model of a network; processing thelanguage model based on inputting a security action to generate a damageevent list; processing the damage event list to extract security eventsand generate a security event list; and outputting the damage event listand the security event list to a network administrator.

According to an embodiment of the present invention, a computer programproduct for identifying architectures of machine learning models meetinga user defined constraint, the computer program product comprising: oneor more non-transitory computer readable storage media and programinstructions stored on the one or more non-transitory computer readablestorage media, the program instructions comprising: program instructionsto analyze an unlabeled set of network data to create a language modelof a network; program instructions to process the language model basedon inputting a security action to generate a damage event list; programinstructions to process the damage event list to extract security eventsand generate a security event list; and program instructions to outputthe damage event list and the security event list to a networkadministrator.

According to an embodiment of the present invention, a computer systemfor identifying architectures of machine learning models meeting a userdefined constraint, the computer system comprising: one or more computerprocessors; one or more non-transitory computer readable storage media;and program instructions stored on the one or more non-transitorycomputer readable storage media, the program instructions comprising:program instructions to analyze an unlabeled set of network data tocreate a language model of a network; program instructions to processthe language model based on inputting a security action to generate adamage event list; program instructions to process the damage event listto extract security events and generate a security event list; andprogram instructions to output the damage event list and the securityevent list to a network administrator.

Other aspects and embodiments of the present invention will becomeapparent from the following detailed description, which, when taken inconjunction with the drawings, illustrate by way of example theprinciples of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a cloud computing environment, according to embodimentsof the present invention.

FIG. 2 depicts abstraction model layers, according to embodiments of thepresent invention.

FIG. 3 is a high-level architecture, according to embodiments of thepresent invention.

FIG. 4 is an exemplary detailed architecture, according to embodimentsof the present invention.

FIG. 5 is a flowchart of a method, according to embodiments of thepresent invention.

FIG. 6 is a block diagram of internal and external components of a dataprocessing system in which embodiments described herein may beimplemented, according to embodiments of the present invention.

DETAILED DESCRIPTION

The following description is made for the purpose of illustrating thegeneral principles of the present invention and is not meant to limitthe inventive concepts claimed herein. Further, particular featuresdescribed herein can be used in combination with other describedfeatures in each of the various possible combinations and permutations.

Unless otherwise specifically defined herein, all terms are to be giventheir broadest possible interpretation including meanings implied fromthe specification as well as meanings understood by those skilled in theart and/or as defined in dictionaries, treatises, etc.

It must also be noted that, as used in the specification and theappended claims, the singular forms “a,” “an” and “the” include pluralreferents unless otherwise specified. It will be further understood thatthe terms “comprises” and/or “comprising,” when used in thisspecification, specify the presence of stated features, integers, steps,operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof.

The following description discloses several embodiments of estimatingcomputer system security response effectiveness and impact on computersystem availability based on language modeling of network flows andsystem events. It should be noted that the term software, as usedherein, includes any type of computer instructions such as, but notlimited to, firmware, microcode, etc.

Embodiments of the present invention can train a language model based ona computer system's historical data related to security breaches of thecomputer system and its associated network connectivity. Theembodiments, in one aspect, can retrieve system historical data from adata repository. The embodiments, in another aspect, can create languagesequences. For example, the embodiments can identify the components ofthe historical data such as, but not limited to, events and associateddata and the order in which the events occurred. In another aspect, theembodiments can use the language sequences to train one or more modelsfor estimating security response effectiveness and the impact oncomputer system availability.

Further, embodiments of the present invention can provide a technique ofperforming an analysis of an unlabeled dataset of network flow trafficand system events based on the trained language model analysis of theunlabeled data.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as Follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as Follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as Follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 1 , illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 1 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 2 , a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 1 ) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 2 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 include hardware and software components.Examples of hardware components include mainframes 61; RISC (ReducedInstruction Set Computer) architecture-based servers 62; servers 63;blade servers 64; storage devices 65; and networks and networkingcomponents 66. In some embodiments, software components include networkapplication server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and security response predictor 96.

It should be noted that the embodiments of the present invention mayoperate with a user's permission. Any data may be gathered, stored,analyzed, etc., with a user's consent. In various configurations, atleast some of the embodiments of the present invention are implementedinto an opt-in application, plug-in, etc., as would be understood by onehaving ordinary skill in the art upon reading the present disclosure.

FIG. 3 is a high-level architecture for performing various operations ofFIG. 5 , in accordance with various embodiments. The architecture 300may be implemented in accordance with the present invention in any ofthe environments depicted in FIGS. 1-4 , among others, in variousembodiments. Of course, more or less elements than those specificallydescribed in FIG. 3 may be included in architecture 300, as would beunderstood by one of ordinary skill in the art upon reading the presentdescriptions.

Each of the steps of the method 500 (described in further detail below)may be performed by any suitable component of the architecture 300. Aprocessor, e.g., processing circuit(s), chip(s), and/or module(s)implemented in hardware and/or software, and preferably having at leastone hardware component may be utilized in any device to perform one ormore steps of the method 500 in the architecture 300. Illustrativeprocessors include, but are not limited to, a central processing unit(CPU), an application specific integrated circuit (ASIC), a fieldprogrammable gate array (FPGA), etc., combinations thereof, or any othersuitable computing device known in the art.

Architecture 300 includes a block diagram showing an exemplaryprocessing system for predicting security response effectiveness towhich the invention principles may be applied. The architecture 300comprises a client computer 302, a security response predictioncomponent 308 operational on a server computer 304 and a network 306supporting communication between the client computer 302 and the servercomputer 304.

Client computer 302 can be any computing device on which software isinstalled for which an update is desired or required. Client computer302 can be a standalone computing device, management server, a webserver, a mobile computing device, or any other electronic device orcomputing system capable of receiving, sending, and processing data. Inother embodiments, client computer 302 can represent a server computingsystem utilizing multiple computers as a server system. In anotherembodiment, client computer 302 can be a laptop computer, a tabletcomputer, a netbook computer, a personal computer, a desktop computer orany programmable electronic device capable of communicating with othercomputing devices (not shown) within user persona generation environmentvia network 306.

In another embodiment, client computer 302 represents a computing systemutilizing clustered computers and components (e.g., database servercomputers, application server computers, etc.) that act as a single poolof seamless resources when accessed within install-time validationenvironment of architecture 300. Client computer 302 can includeinternal and external hardware components, as depicted and described infurther detail with respect to FIG. 5 .

Server computer 304 can be a standalone computing device, managementserver, a web server, a mobile computing device, or any other electronicdevice or computing system capable of receiving, sending, and processingdata. In other embodiments, server computer 304 can represent a servercomputing system utilizing multiple computers as a server system. Inanother embodiment, server computer 304 can be a laptop computer, atablet computer, a netbook computer, a personal computer, a desktopcomputer, or any programmable electronic device capable of communicatingwith other computing devices (not shown) within install-time validationenvironment of architecture 300 via network 306.

Network 306 can be, for example, a local area network (LAN), a wide areanetwork (WAN) such as the Internet, or a combination of the two, and caninclude wired, wireless, or fiber optic connections. In general, network306 can be any combination of connections and protocols that willsupport communications between client computer 302 and server computer304.

Security response prediction component 308, operational on servercomputer 304, can analyze historical data associated with internaltraffic of a computer system to predict the effectiveness and impact onavailability of a security threat response to a computer system.Embodiments of the present invention can use the historical data togenerate and train a language model reflecting the behavior of thecomputer system. The embodiments can then exercise the generatedlanguage model with proposed security threat scenarios and predict theeffectiveness of a response to the proposed security threat and theimpact the response would have on system availability without exposingthe computer system to the threat or the response.

FIG. 4 is an exemplary detailed architecture for performing variousoperations of FIG. 5 , in accordance with various embodiments. Thearchitecture 400 may be implemented in accordance with the presentinvention in any of the environments depicted in FIGS. 1-3 and 5 , amongothers, in various embodiments. Of course, more or less elements thanthose specifically described in FIG. 4 may be included in architecture400, as would be understood by one of skill in the art upon reading thepresent descriptions.

Each of the steps of the method 500 (described in further detail below)may be performed by any suitable component of the architecture 400. Aprocessor, e.g., processing circuit(s), chip(s), and/or module(s)implemented in hardware and/or software, and preferably having at leastone hardware component, may be utilized in any device to perform one ormore steps of the method 500 in the architecture 400. Illustrativeprocessors include, but are not limited to, a central processing unit(CPU), an application specific integrated circuit (ASIC), a fieldprogrammable gate array (FPGA), etc., combinations thereof, or any othersuitable computing device known in the art.

Architecture 400 provides a detailed view of at least some of themodules of architecture 300. Architecture 400 can comprise a securityresponse prediction component 308, which can further comprise a languagemodel generation component 402, a language model training component 404,a language model analysis component 406 and a historical data component408.

In one embodiment, language model generation component 402 can create alanguage model based on the input unlabeled set of network flow trafficand system events. A language model is a probability distribution oversequences of traffic flows and system events. Considering a networkevent sequence, for example of length “m,” the language model assigns aprobability P (event-1, . . . , event-m) to the whole network eventsequence. The language model provides context to distinguish betweennetwork events that may appear similar. The language model may include,but is not limited to, a unigram model, an n-gram model (e.g., bigram,trigram, etc.) including bidirectional representations, an exponentialmodel, a neural network and a positional model. In another aspect, thelanguage model can predict events that are missing, replaced, redundantor out of order.

In one embodiment, language model training component 404 can train alanguage model created by language model generation component 402 basedon input from historical data associated with the computer networksystem. It should be noted that the historical data can include, but isnot limited to network flow traffic, system events, security analytics,etc., e.g., firewall alerts on a connection to a bitcoin mining server.In another embodiment, language model training component 404 can trainthe language model to recognize inconsistent network events, e.g.,predict an event has been omitted from a series of network events. Forexample, in a sequence of events such as “a,b,c,?,e,” language modeltraining component 404 can train the language model to predict that theevent “d” was omitted from the sequence of network events. In anotheraspect of an embodiment, language model training component 404 can trainthe language model to output a probability vector of all possiblenetwork events. In a further aspect of an embodiment, language modeltraining component 404 can train the language model to allow randomsampling of sub-sequences of network events. For example, for a sequenceof network events such as “a,b,c,d,e,f,g,h,” language model trainingcomponent 404 can train the language model to sample the sub-sequence“a,c,e,g.” In another aspect language model analysis component 406 cangenerate a probability vector of possible events.

In one embodiment, language model analysis component 406 can determinethe availability damage associated with blocking an event, e.g., “X.”For example, when provided a blocking action, e.g., a port number, aninternet protocol (IP) address, etc. to block, language model analysiscomponent 406 can calculate one or more language model scores based onsequences selected from the unlabeled data set of network traffic flow,system events, analytic events, etc., i.e., the historical data of thecomputer system under analysis. Language model analysis component 406can sample sequences including “X,” e.g., “a,b,c,X,Y” and for thesamples, language model analysis component 406 can determine theprobability of “X” in the sequence and if the average of theprobabilities is greater than a predetermined threshold, then indicatethat blocking “X” will also block “Y.” It should be noted that thedetermination should be repeated for other events in the sequence thatare damaged by “X,” e.g., “Y.” The output of this analysis is a list ofdamaged events.

In another aspect, language model analysis component 406 can determinethe efficiency of blocking “X” based on processing the list of eventsdamaged by blocking “X” and creating a list of blocked events whereinthe events added to the list of blocked events can be security analyticsevents. Other aspects of the embodiment can include a security hardeningbased on permanently block an availability damage event if the event isbelow a predetermined threshold. In another aspect, more than onelanguage model can be created based on omitting different events. Forexample, omitting “d” from the sequence of events “a,b,c,d,e” andomitting “a” from the sequence of events “a,b,c,d.”

In one embodiment, language model analysis component 406 can process thelanguage model with a security response of interest and desiredmalicious event sequences. Based on this language model processing, thelanguage model analysis component 406 can predict an effectivenessfactor, used to generate an effectiveness score. It should be noted thatthe greater the effectiveness score the greater the effectiveness of thesecurity response against the malicious event sequences.

In another embodiment, language model analysis component 406 can processthe language model with a security response of interest and desiredbenign event sequences. Based on this language model processing, thelanguage model analysis component 406 can predict a reduction factor,used to generate an availability score. It should be noted that thelower the availability score the less the impact of the securityresponse on computer network system availability.

In one embodiment, historical data component 408 provides unlabeled datasets of computer network traffic flow and system events. Historical datacomponent 408 can maintain unlabeled data sets associated with differentcomputer networks. In another aspect of an embodiment, historical datacomponent 408 can store language models generated by language modelgeneration component 402 and provide the language models for use on anycomputer system having access to historical data component 408. Inanother aspect, historical data component 408 can store the results ofprocessed language models, e.g., reduction factors, effectivenessfactors, probability vectors, effectiveness scores, availability scores,etc., for use by network administrators having access to historical datacomponent 408.

FIG. 5 is an exemplary flowchart of a method 500 for evaluating computersecurity responses based on response effectiveness and response impacton computer network system availability. At step 502, an embodiment cananalyze, via language model analysis component 406, unlabeled networkdata, via historical data component 408, to create a language model, vialanguage model generation component 402, of the network. At step 504,the embodiment can process the language model, via language modelanalysis component 406, to generate a damage event list. At step 506,the embodiment can process the damage event list, via language modelanalysis component 406, to generate a security event list. At step 508,the embodiment can output, via security response prediction component308, the damage event list and the security event list to a networkadministrator.

FIG. 6 depicts computer system 600, an example computer systemrepresentative of client computer 302 and server computer 304. Computersystem 600 includes communications fabric 602, which providescommunications between computer processor(s) 604, memory 606, persistentstorage 608, communications unit 610, and input/output (I/O)interface(s) 612. Communications fabric 602 can be implemented with anyarchitecture designed for passing data and/or control informationbetween processors (such as microprocessors, communications and networkprocessors, etc.), system memory, peripheral devices, and any otherhardware components within a system. For example, communications fabric602 can be implemented with one or more buses.

Computer system 600 includes processors 604, cache 616, memory 606,persistent storage 608, communications unit 610, input/output (I/O)interface(s) 612 and communications fabric 602. Communications fabric602 provides communications between cache 616, memory 606, persistentstorage 608, communications unit 610, and input/output (I/O)interface(s) 612. Communications fabric 602 can be implemented with anyarchitecture designed for passing data and/or control informationbetween processors (such as microprocessors, communications and networkprocessors, etc.), system memory, peripheral devices, and any otherhardware components within a system. For example, communications fabric602 can be implemented with one or more buses or a crossbar switch.

Memory 606 and persistent storage 608 are computer readable storagemedia. In this embodiment, memory 606 includes random access memory(RAM). In general, memory 606 can include any suitable volatile ornon-volatile computer readable storage media. Cache 616 is a fast memorythat enhances the performance of processors 604 by holding recentlyaccessed data, and data near recently accessed data, from memory 606.

Program instructions and data used to practice embodiments of thepresent invention may be stored in persistent storage 608 and in memory606 for execution by one or more of the respective processors 604 viacache 616. In an embodiment, persistent storage 608 includes a magnetichard disk drive. Alternatively, or in addition to a magnetic hard diskdrive, persistent storage 608 can include a solid state hard drive, asemiconductor storage device, read-only memory (ROM), erasableprogrammable read-only memory (EPROM), flash memory, or any othercomputer readable storage media that is capable of storing programinstructions or digital information.

The media used by persistent storage 608 may also be removable. Forexample, a removable hard drive may be used for persistent storage 608.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer readable storage medium that is also part of persistent storage608.

Communications unit 610, in these examples, provides for communicationswith other data processing systems or devices. In these examples,communications unit 610 includes one or more network interface cards.Communications unit 610 may provide communications through the use ofeither or both physical and wireless communications links. Programinstructions and data used to practice embodiments of the presentinvention may be downloaded to persistent storage 608 throughcommunications unit 610.

I/O interface(s) 612 allows for input and output of data with otherdevices that may be connected to each computer system. For example, I/Ointerface 612 may provide a connection to external devices 618 such as akeyboard, keypad, a touch screen, and/or some other suitable inputdevice. External devices 618 can also include portable computer readablestorage media such as, for example, thumb drives, portable optical ormagnetic disks, and memory cards. Software and data used to practiceembodiments of the present invention can be stored on such portablecomputer readable storage media and can be loaded onto persistentstorage 608 via I/O interface(s) 612. I/O interface(s) 612 also connectto display 620.

Display 620 provides a mechanism to display data to a user and may be,for example, a computer monitor.

The components described herein are identified based upon theapplication for which they are implemented in a specific embodiment ofthe invention. However, it should be appreciated that any particularcomponent nomenclature herein is used merely for convenience, and thusthe invention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerreadable program instructions may also be stored in a computer readablestorage medium that can direct a computer, a programmable dataprocessing apparatus, and/or other devices to function in a particularmanner, such that the computer readable storage medium havinginstructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be accomplished as one step, executed concurrently,substantially concurrently, in a partially or wholly temporallyoverlapping manner, or the blocks may sometimes be executed in thereverse order, depending upon the functionality involved. It will alsobe noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

Moreover, a system according to various embodiments may include aprocessor and logic integrated with and/or executable by the processor,the logic being configured to perform one or more of the process stepsrecited herein. By integrated with, what is meant is that the processorhas logic embedded therewith as hardware logic, such as an applicationspecific integrated circuit (ASIC), a FPGA, etc. By executable by theprocessor, what is meant is that the logic is hardware logic; softwarelogic such as firmware, part of an operating system, part of anapplication program; etc., or some combination of hardware and softwarelogic that is accessible by the processor and configured to cause theprocessor to perform some functionality upon execution by the processor.Software logic may be stored on local and/or remote memory of any memorytype, as known in the art. Any processor known in the art may be used,such as a software processor module and/or a hardware processor such asan ASIC, a FPGA, a central processing unit (CPU), an integrated circuit(IC), a graphics processing unit (GPU), etc.

It will be clear that the various features of the foregoing systemsand/or methodologies may be combined in any way, creating a plurality ofcombinations from the descriptions presented above.

It will be further appreciated that embodiments of the present inventionmay be provided in the form of a service deployed on behalf of acustomer to offer service on demand.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration but are not intended tobe exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer-implemented method for evaluating acomputer security response, the computer-implemented method comprising:analyzing an unlabeled set of network data to create a language model ofa network; processing the language model based on inputting a securityaction to generate a damage event list; processing the damage event listto extract security events and generate a security event list; andoutputting the damage event list and the security event list to anetwork administrator.
 2. The computer-implemented method of claim 1,wherein the unlabeled set of network data is network events comprisingtraffic flow events, system events and security events.
 3. Thecomputer-implemented method of claim 2, wherein the analyzing comprisespredicting an omitted event from a sequence of network events.
 4. Thecomputer-implemented method of claim 1, wherein the language modelpredicts the damage events based on a probability vector of all possibleevents.
 5. The computer-implemented method of claim 3, wherein theanalyzing further comprises the use of a random sample of sub-sequencesof the sequence of network events.
 6. The computer-implemented method ofclaim 1, further comprising: generating an effectiveness score based onthe use of a malicious sequence, wherein an effectiveness score above apredetermined threshold indicates an associated security response iseffective; and generating a network availability score based on the useof a benign sequence, wherein a network availability score below apredetermined threshold indicates the associated security response hasacceptable impact on the network availability.
 7. Thecomputer-implemented method of claim 6, wherein a network availabilityscore is adjusted based on a count of similar security responseoccurrences in the unlabeled set of network data.
 8. A computer programproduct for identifying architectures of machine learning models meetinga user defined constraint, the computer program product comprising: oneor more non-transitory computer readable storage media and programinstructions stored on the one or more non-transitory computer readablestorage media, the program instructions comprising: program instructionsto analyze an unlabeled set of network data to create a language modelof a network; program instructions to process the language model basedon inputting a security action to generate a damage event list; programinstructions to process the damage event list to extract security eventsand generate a security event list; and program instructions to outputthe damage event list and the security event list to a networkadministrator.
 9. The computer program product of claim 8, wherein theunlabeled set of network data is network events comprising traffic flowevents, system events and security events.
 10. The computer programproduct of claim 9, wherein the analyzing comprises predicting anomitted event from a sequence of network events.
 11. The computerprogram product of claim 8, wherein the language model predicts thedamage events based on a probability vector of all possible events. 12.The computer program product of claim 10, wherein the analyzing furthercomprises the use of a random sample of sub-sequences of the sequence ofnetwork events.
 13. The computer program product of claim 8, furthercomprising: program instructions to generate an effectiveness scorebased on the use of a malicious sequence, wherein an effectiveness scoreabove a predetermined threshold indicates an associated securityresponse is effective; and program instructions to generate a networkavailability score based on the use of a benign sequence, wherein anetwork availability score below a predetermined threshold indicates theassociated security response has acceptable impact on the networkavailability.
 14. The computer program product of claim 13, wherein anetwork availability score is adjusted based on a count of similarsecurity response occurrences in the unlabeled set of network data. 15.A computer system for identifying architectures of machine learningmodels meeting a user defined constraint, the computer systemcomprising: one or more computer processors; one or more non-transitorycomputer readable storage media; and program instructions stored on theone or more non-transitory computer readable storage media, the programinstructions comprising: program instructions to analyze an unlabeledset of network data to create a language model of a network; programinstructions to process the language model based on inputting a securityaction to generate a damage event list; program instructions to processthe damage event list to extract security events and generate a securityevent list; and program instructions to output the damage event list andthe security event list to a network administrator.
 16. The computersystem of claim 15, wherein the unlabeled set of network data is networkevents comprising traffic flow events, system events and securityevents.
 17. The computer system of claim 16, wherein the analyzingcomprises predicting an omitted event from a sequence of network eventsand the use of a random sample of sub-sequences of the sequence ofnetwork events.
 18. The computer system of claim 15, wherein thelanguage model predicts the damage events based on a probability vectorof all possible events.
 19. The computer system of claim 15, furthercomprising: program instructions to generate an effectiveness scorebased on the use of a malicious sequence, wherein an effectiveness scoreabove a predetermined threshold indicates an associated securityresponse is effective; and program instructions to generate a networkavailability score based on the use of a benign sequence, wherein anetwork availability score below a predetermined threshold indicates theassociated security response has acceptable impact on the networkavailability.
 20. The computer system of claim 19, wherein a networkavailability score is adjusted based on a count of similar securityresponse occurrences in the unlabeled set of network data.